Overview

Dataset statistics

Number of variables17
Number of observations426983
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.4 MiB
Average record size in memory136.0 B

Variable types

Categorical6
Numeric4
DateTime1
Text6

Alerts

Tribunal has constant value "TJRN"Constant
UF has constant value "RN"Constant
Formato is highly imbalanced (> 99.9%)Imbalance

Reproduction

Analysis started2024-05-16 14:49:21.151516
Analysis finished2024-05-16 14:50:00.168959
Duration39.02 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Ano
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2023
367026 
2024
59957 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1707932
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 367026
86.0%
2024 59957
 
14.0%

Length

2024-05-16T14:50:00.365853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:00.704181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2023 367026
86.0%
2024 59957
 
14.0%

Most occurring characters

ValueCountFrequency (%)
2 853966
50.0%
0 426983
25.0%
3 367026
21.5%
4 59957
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 853966
50.0%
0 426983
25.0%
3 367026
21.5%
4 59957
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 853966
50.0%
0 426983
25.0%
3 367026
21.5%
4 59957
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 853966
50.0%
0 426983
25.0%
3 367026
21.5%
4 59957
 
3.5%

Mes
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9356555
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:00.988756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5734973
Coefficient of variation (CV)0.6020392
Kurtosis-1.3072456
Mean5.9356555
Median Absolute Deviation (MAD)3
Skewness0.15259113
Sum2534424
Variance12.769883
MonotonicityNot monotonic
2024-05-16T14:50:01.446893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 59311
13.9%
1 50432
11.8%
3 35667
8.4%
8 35058
8.2%
10 33267
7.8%
11 33239
7.8%
5 33172
7.8%
7 32278
7.6%
9 31194
7.3%
6 29501
6.9%
Other values (2) 53864
12.6%
ValueCountFrequency (%)
1 50432
11.8%
2 59311
13.9%
3 35667
8.4%
4 27040
6.3%
5 33172
7.8%
6 29501
6.9%
7 32278
7.6%
8 35058
8.2%
9 31194
7.3%
10 33267
7.8%
ValueCountFrequency (%)
12 26824
6.3%
11 33239
7.8%
10 33267
7.8%
9 31194
7.3%
8 35058
8.2%
7 32278
7.6%
6 29501
6.9%
5 33172
7.8%
4 27040
6.3%
3 35667
8.4%
Distinct423
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Minimum2023-01-01 00:00:00
Maximum2024-02-29 00:00:00
2024-05-16T14:50:01.921092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:50:02.912517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tribunal
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
TJRN
426983 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1707932
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTJRN
2nd rowTJRN
3rd rowTJRN
4th rowTJRN
5th rowTJRN

Common Values

ValueCountFrequency (%)
TJRN 426983
100.0%

Length

2024-05-16T14:50:03.962666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:04.277020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
tjrn 426983
100.0%

Most occurring characters

ValueCountFrequency (%)
T 426983
25.0%
J 426983
25.0%
R 426983
25.0%
N 426983
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 426983
25.0%
J 426983
25.0%
R 426983
25.0%
N 426983
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 426983
25.0%
J 426983
25.0%
R 426983
25.0%
N 426983
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1707932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 426983
25.0%
J 426983
25.0%
R 426983
25.0%
N 426983
25.0%
Distinct268
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:04.797538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length112
Median length88
Mean length45.389917
Min length11

Characters and Unicode

Total characters19380723
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU
2nd rowJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU
3rd rowJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU
4th rowJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU
5th rowJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU
ValueCountFrequency (%)
da 478106
 
14.1%
de 376229
 
11.1%
comarca 340052
 
10.0%
vara 196904
 
5.8%
juizado 141509
 
4.2%
natal 141178
 
4.2%
cível 139192
 
4.1%
especial 136073
 
4.0%
fazenda 119100
 
3.5%
pública 119100
 
3.5%
Other values (219) 1198911
35.4%
2024-05-16T14:50:05.763900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3256202
16.8%
2959933
15.3%
E 1397171
 
7.2%
C 1365984
 
7.0%
D 1335527
 
6.9%
R 1096435
 
5.7%
I 926252
 
4.8%
O 918113
 
4.7%
L 742213
 
3.8%
M 697089
 
3.6%
Other values (42) 4685804
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19380723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3256202
16.8%
2959933
15.3%
E 1397171
 
7.2%
C 1365984
 
7.0%
D 1335527
 
6.9%
R 1096435
 
5.7%
I 926252
 
4.8%
O 918113
 
4.7%
L 742213
 
3.8%
M 697089
 
3.6%
Other values (42) 4685804
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19380723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3256202
16.8%
2959933
15.3%
E 1397171
 
7.2%
C 1365984
 
7.0%
D 1335527
 
6.9%
R 1096435
 
5.7%
I 926252
 
4.8%
O 918113
 
4.7%
L 742213
 
3.8%
M 697089
 
3.6%
Other values (42) 4685804
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19380723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3256202
16.8%
2959933
15.3%
E 1397171
 
7.2%
C 1365984
 
7.0%
D 1335527
 
6.9%
R 1096435
 
5.7%
I 926252
 
4.8%
O 918113
 
4.7%
L 742213
 
3.8%
M 697089
 
3.6%
Other values (42) 4685804
24.2%

Codigo Orgao
Real number (ℝ)

Distinct268
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39516.858
Minimum4439
Maximum87922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:06.139799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4439
5-th percentile4747
Q15983
median17461
Q376682
95-th percentile84550
Maximum87922
Range83483
Interquartile range (IQR)70699

Descriptive statistics

Standard deviation33285.402
Coefficient of variation (CV)0.84230892
Kurtosis-1.7750184
Mean39516.858
Median Absolute Deviation (MAD)12735
Skewness0.23261749
Sum1.6873027 × 1010
Variance1.107918 × 109
MonotonicityNot monotonic
2024-05-16T14:50:06.520846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84118 10670
 
2.5%
85202 10619
 
2.5%
82205 10313
 
2.4%
18352 6970
 
1.6%
17460 6502
 
1.5%
17461 6429
 
1.5%
76687 6338
 
1.5%
75497 6281
 
1.5%
75496 6145
 
1.4%
11083 4044
 
0.9%
Other values (258) 352672
82.6%
ValueCountFrequency (%)
4439 1261
0.3%
4705 1200
0.3%
4709 1442
0.3%
4712 1355
0.3%
4716 1801
0.4%
4719 1344
0.3%
4724 1204
0.3%
4725 1424
0.3%
4726 992
0.2%
4729 1313
0.3%
ValueCountFrequency (%)
87922 20
 
< 0.1%
87823 489
0.1%
87475 1
 
< 0.1%
86589 821
0.2%
86588 684
0.2%
86569 199
 
< 0.1%
86568 148
 
< 0.1%
86567 115
 
< 0.1%
86566 211
 
< 0.1%
86565 126
 
< 0.1%

id_municipio
Real number (ℝ)

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2996.6063
Minimum22
Maximum5597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:06.922187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile365
Q13159
median3201
Q33201
95-th percentile4611
Maximum5597
Range5575
Interquartile range (IQR)42

Descriptive statistics

Standard deviation1030.0864
Coefficient of variation (CV)0.343751
Kurtosis2.0914703
Mean2996.6063
Median Absolute Deviation (MAD)0
Skewness-1.1208841
Sum1.2794999 × 109
Variance1061078
MonotonicityNot monotonic
2024-05-16T14:50:07.272087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3201 228104
53.4%
3159 35715
 
8.4%
3576 23806
 
5.6%
875 7484
 
1.8%
278 6938
 
1.6%
3612 6692
 
1.6%
1192 6560
 
1.5%
4611 6528
 
1.5%
1492 6297
 
1.5%
28 6202
 
1.5%
Other values (45) 92657
21.7%
ValueCountFrequency (%)
22 1403
 
0.3%
28 6202
1.5%
108 1969
 
0.5%
123 1785
 
0.4%
238 1906
 
0.4%
278 6938
1.6%
365 3311
0.8%
489 3408
0.8%
875 7484
1.8%
1016 2734
 
0.6%
ValueCountFrequency (%)
5597 1805
0.4%
5326 2556
0.6%
5307 2349
0.6%
5205 1733
0.4%
5080 2252
0.5%
4852 1140
0.3%
4798 1251
0.3%
4772 2347
0.5%
4710 1667
0.4%
4699 2613
0.6%
Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:07.708198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length5
Mean length7.1547228
Min length3

Characters and Unicode

Total characters3054945
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAÇU
2nd rowAÇU
3rd rowAÇU
4th rowAÇU
5th rowAÇU
ValueCountFrequency (%)
natal 228104
43.4%
mossoro 35715
 
6.8%
parnamirim 23806
 
4.5%
sao 16661
 
3.2%
do 12620
 
2.4%
caico 7484
 
1.4%
cruz 7225
 
1.4%
apodi 6938
 
1.3%
ferros 6692
 
1.3%
dos 6692
 
1.3%
Other values (67) 173427
33.0%
2024-05-16T14:50:08.411952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 762216
25.0%
N 331038
10.8%
T 272916
 
8.9%
L 259475
 
8.5%
O 249068
 
8.2%
R 196904
 
6.4%
S 153417
 
5.0%
M 149596
 
4.9%
I 138757
 
4.5%
98381
 
3.2%
Other values (15) 443177
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3054945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 762216
25.0%
N 331038
10.8%
T 272916
 
8.9%
L 259475
 
8.5%
O 249068
 
8.2%
R 196904
 
6.4%
S 153417
 
5.0%
M 149596
 
4.9%
I 138757
 
4.5%
98381
 
3.2%
Other values (15) 443177
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3054945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 762216
25.0%
N 331038
10.8%
T 272916
 
8.9%
L 259475
 
8.5%
O 249068
 
8.2%
R 196904
 
6.4%
S 153417
 
5.0%
M 149596
 
4.9%
I 138757
 
4.5%
98381
 
3.2%
Other values (15) 443177
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3054945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 762216
25.0%
N 331038
10.8%
T 272916
 
8.9%
L 259475
 
8.5%
O 249068
 
8.2%
R 196904
 
6.4%
S 153417
 
5.0%
M 149596
 
4.9%
I 138757
 
4.5%
98381
 
3.2%
Other values (15) 443177
14.5%

UF
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
RN
426983 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters853966
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRN
2nd rowRN
3rd rowRN
4th rowRN
5th rowRN

Common Values

ValueCountFrequency (%)
RN 426983
100.0%

Length

2024-05-16T14:50:08.694360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:08.916381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rn 426983
100.0%

Most occurring characters

ValueCountFrequency (%)
R 426983
50.0%
N 426983
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 426983
50.0%
N 426983
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 426983
50.0%
N 426983
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 426983
50.0%
N 426983
50.0%

Grau
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
G1
182792 
JE
163691 
G2
48730 
TR
31770 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters853966
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG1
2nd rowG1
3rd rowG1
4th rowG1
5th rowG1

Common Values

ValueCountFrequency (%)
G1 182792
42.8%
JE 163691
38.3%
G2 48730
 
11.4%
TR 31770
 
7.4%

Length

2024-05-16T14:50:09.154983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:09.425334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
g1 182792
42.8%
je 163691
38.3%
g2 48730
 
11.4%
tr 31770
 
7.4%

Most occurring characters

ValueCountFrequency (%)
G 231522
27.1%
1 182792
21.4%
J 163691
19.2%
E 163691
19.2%
2 48730
 
5.7%
T 31770
 
3.7%
R 31770
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 231522
27.1%
1 182792
21.4%
J 163691
19.2%
E 163691
19.2%
2 48730
 
5.7%
T 31770
 
3.7%
R 31770
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 231522
27.1%
1 182792
21.4%
J 163691
19.2%
E 163691
19.2%
2 48730
 
5.7%
T 31770
 
3.7%
R 31770
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 853966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 231522
27.1%
1 182792
21.4%
J 163691
19.2%
E 163691
19.2%
2 48730
 
5.7%
T 31770
 
3.7%
R 31770
 
3.7%

Formato
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Eletrônico
426974 
Físico
 
9

Length

Max length10
Median length10
Mean length9.9999157
Min length6

Characters and Unicode

Total characters4269794
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEletrônico
2nd rowEletrônico
3rd rowEletrônico
4th rowEletrônico
5th rowEletrônico

Common Values

ValueCountFrequency (%)
Eletrônico 426974
> 99.9%
Físico 9
 
< 0.1%

Length

2024-05-16T14:50:09.747640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:10.006946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
eletrônico 426974
> 99.9%
físico 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 426983
10.0%
c 426983
10.0%
o 426983
10.0%
E 426974
10.0%
l 426974
10.0%
e 426974
10.0%
t 426974
10.0%
r 426974
10.0%
ô 426974
10.0%
n 426974
10.0%
Other values (3) 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4269794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 426983
10.0%
c 426983
10.0%
o 426983
10.0%
E 426974
10.0%
l 426974
10.0%
e 426974
10.0%
t 426974
10.0%
r 426974
10.0%
ô 426974
10.0%
n 426974
10.0%
Other values (3) 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4269794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 426983
10.0%
c 426983
10.0%
o 426983
10.0%
E 426974
10.0%
l 426974
10.0%
e 426974
10.0%
t 426974
10.0%
r 426974
10.0%
ô 426974
10.0%
n 426974
10.0%
Other values (3) 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4269794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 426983
10.0%
c 426983
10.0%
o 426983
10.0%
E 426974
10.0%
l 426974
10.0%
e 426974
10.0%
t 426974
10.0%
r 426974
10.0%
ô 426974
10.0%
n 426974
10.0%
Other values (3) 27
 
< 0.1%

Procedimento
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Conhecimento não criminal
280996 
Execução judicial
78067 
Conhecimento criminal
42161 
Execução fiscal
 
14224
Execução extrajudicial não fiscal
 
11527

Length

Max length41
Median length25
Mean length23.025505
Min length15

Characters and Unicode

Total characters9831499
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConhecimento criminal
2nd rowConhecimento criminal
3rd rowConhecimento criminal
4th rowConhecimento criminal
5th rowConhecimento criminal

Common Values

ValueCountFrequency (%)
Conhecimento não criminal 280996
65.8%
Execução judicial 78067
 
18.3%
Conhecimento criminal 42161
 
9.9%
Execução fiscal 14224
 
3.3%
Execução extrajudicial não fiscal 11527
 
2.7%
Execução penal não privativa de liberdade 8
 
< 0.1%

Length

2024-05-16T14:50:10.428931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T14:50:11.105326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
conhecimento 323157
27.9%
criminal 323157
27.9%
não 292531
25.3%
execução 103826
 
9.0%
judicial 78067
 
6.7%
fiscal 25751
 
2.2%
extrajudicial 11527
 
1.0%
penal 8
 
< 0.1%
privativa 8
 
< 0.1%
de 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 1262010
12.8%
i 1174434
11.9%
o 1042671
10.6%
c 865485
8.8%
e 761699
 
7.7%
731065
 
7.4%
m 646314
 
6.6%
a 450061
 
4.6%
l 438518
 
4.5%
ã 396357
 
4.0%
Other values (15) 2062885
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9831499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1262010
12.8%
i 1174434
11.9%
o 1042671
10.6%
c 865485
8.8%
e 761699
 
7.7%
731065
 
7.4%
m 646314
 
6.6%
a 450061
 
4.6%
l 438518
 
4.5%
ã 396357
 
4.0%
Other values (15) 2062885
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9831499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1262010
12.8%
i 1174434
11.9%
o 1042671
10.6%
c 865485
8.8%
e 761699
 
7.7%
731065
 
7.4%
m 646314
 
6.6%
a 450061
 
4.6%
l 438518
 
4.5%
ã 396357
 
4.0%
Other values (15) 2062885
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9831499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1262010
12.8%
i 1174434
11.9%
o 1042671
10.6%
c 865485
8.8%
e 761699
 
7.7%
731065
 
7.4%
m 646314
 
6.6%
a 450061
 
4.6%
l 438518
 
4.5%
ã 396357
 
4.0%
Other values (15) 2062885
21.0%
Distinct389175
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:11.867964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length25
Median length25
Mean length24.251853
Min length19

Characters and Unicode

Total characters10355129
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353103 ?
Unique (%)82.7%

Sample

1st row0800805-11.2023.8.20.5100
2nd row0803258-76.2023.8.20.5100
3rd row0803267-38.2023.8.20.5100
4th row0800636-24.2023.8.20.5100
5th row0800293-28.2023.8.20.5100
ValueCountFrequency (%)
0804566-32.2023.8.20.5106 4
 
< 0.1%
0800436-97.2023.8.20.5138 4
 
< 0.1%
0801317-88.2023.8.20.5004 4
 
< 0.1%
0805410-94.2023.8.20.5004 4
 
< 0.1%
0800778-25.2023.8.20.5004 4
 
< 0.1%
0801969-19.2023.8.20.5162 4
 
< 0.1%
0801683-24.2023.8.20.5103 4
 
< 0.1%
0801702-30.2023.8.20.5103 4
 
< 0.1%
0804533-57.2023.8.20.5004 4
 
< 0.1%
0801034-65.2023.8.20.5004 4
 
< 0.1%
Other values (389165) 426943
> 99.9%
2024-05-16T14:50:12.871398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2190255
21.2%
2 1511318
14.6%
. 1494968
14.4%
8 987382
9.5%
1 765091
 
7.4%
5 685410
 
6.6%
3 551056
 
5.3%
4 431562
 
4.2%
- 373742
 
3.6%
6 309286
 
3.0%
Other values (9) 1055059
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10355129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2190255
21.2%
2 1511318
14.6%
. 1494968
14.4%
8 987382
9.5%
1 765091
 
7.4%
5 685410
 
6.6%
3 551056
 
5.3%
4 431562
 
4.2%
- 373742
 
3.6%
6 309286
 
3.0%
Other values (9) 1055059
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10355129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2190255
21.2%
2 1511318
14.6%
. 1494968
14.4%
8 987382
9.5%
1 765091
 
7.4%
5 685410
 
6.6%
3 551056
 
5.3%
4 431562
 
4.2%
- 373742
 
3.6%
6 309286
 
3.0%
Other values (9) 1055059
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10355129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2190255
21.2%
2 1511318
14.6%
. 1494968
14.4%
8 987382
9.5%
1 765091
 
7.4%
5 685410
 
6.6%
3 551056
 
5.3%
4 431562
 
4.2%
- 373742
 
3.6%
6 309286
 
3.0%
Other values (9) 1055059
10.2%
Distinct22410
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:13.400831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1327
Median length75
Mean length8.3476696
Min length2

Characters and Unicode

Total characters3564313
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14169 ?
Unique (%)3.3%

Sample

1st row{10508,3659}
2nd row{3386}
3rd row{3542}
4th row{3573,3402,3395}
5th row{3386}
ValueCountFrequency (%)
10671 17844
 
4.2%
6226 15053
 
3.5%
9582 13724
 
3.2%
10299 10844
 
2.5%
5952 10108
 
2.4%
7664 7270
 
1.7%
10433 6805
 
1.6%
7769 6605
 
1.5%
4840 6351
 
1.5%
9607 6211
 
1.5%
Other values (22400) 326168
76.4%
2024-05-16T14:50:14.285238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
{ 426983
12.0%
} 426983
12.0%
1 399395
11.2%
7 358370
10.1%
0 331870
9.3%
9 272580
7.6%
6 243402
6.8%
2 232798
6.5%
4 214190
6.0%
3 181849
 
5.1%
Other values (3) 475893
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3564313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
{ 426983
12.0%
} 426983
12.0%
1 399395
11.2%
7 358370
10.1%
0 331870
9.3%
9 272580
7.6%
6 243402
6.8%
2 232798
6.5%
4 214190
6.0%
3 181849
 
5.1%
Other values (3) 475893
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3564313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
{ 426983
12.0%
} 426983
12.0%
1 399395
11.2%
7 358370
10.1%
0 331870
9.3%
9 272580
7.6%
6 243402
6.8%
2 232798
6.5%
4 214190
6.0%
3 181849
 
5.1%
Other values (3) 475893
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3564313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
{ 426983
12.0%
} 426983
12.0%
1 399395
11.2%
7 358370
10.1%
0 331870
9.3%
9 272580
7.6%
6 243402
6.8%
2 232798
6.5%
4 214190
6.0%
3 181849
 
5.1%
Other values (3) 475893
13.4%
Distinct1001
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:14.754214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length21
Mean length6.0571358
Min length3

Characters and Unicode

Total characters2586294
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique420 ?
Unique (%)0.1%

Sample

1st row{278}
2nd row{278}
3rd row{278}
4th row{278}
5th row{278,283}
ValueCountFrequency (%)
436 57062
 
13.4%
7 51020
 
11.9%
14695 40135
 
9.4%
460 30392
 
7.1%
198 27367
 
6.4%
436,156 24162
 
5.7%
1116 14224
 
3.3%
14695,12078 14073
 
3.3%
7,156 13020
 
3.0%
202 12175
 
2.9%
Other values (991) 143353
33.6%
2024-05-16T14:50:15.692946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
{ 426983
16.5%
} 426983
16.5%
1 304479
11.8%
6 258451
10.0%
4 216555
8.4%
2 148973
 
5.8%
7 147210
 
5.7%
5 123749
 
4.8%
3 121775
 
4.7%
9 112973
 
4.4%
Other values (3) 298163
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2586294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
{ 426983
16.5%
} 426983
16.5%
1 304479
11.8%
6 258451
10.0%
4 216555
8.4%
2 148973
 
5.8%
7 147210
 
5.7%
5 123749
 
4.8%
3 121775
 
4.7%
9 112973
 
4.4%
Other values (3) 298163
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2586294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
{ 426983
16.5%
} 426983
16.5%
1 304479
11.8%
6 258451
10.0%
4 216555
8.4%
2 148973
 
5.8%
7 147210
 
5.7%
5 123749
 
4.8%
3 121775
 
4.7%
9 112973
 
4.4%
Other values (3) 298163
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2586294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
{ 426983
16.5%
} 426983
16.5%
1 304479
11.8%
6 258451
10.0%
4 216555
8.4%
2 148973
 
5.8%
7 147210
 
5.7%
5 123749
 
4.8%
3 121775
 
4.7%
9 112973
 
4.4%
Other values (3) 298163
11.5%
Distinct219
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3462.5091
Minimum7
Maximum15217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:16.248122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q1156
median436
Q31465
95-th percentile14695
Maximum15217
Range15210
Interquartile range (IQR)1309

Descriptive statistics

Standard deviation5610.8482
Coefficient of variation (CV)1.6204573
Kurtosis-0.34934952
Mean3462.5091
Median Absolute Deviation (MAD)355
Skewness1.2519033
Sum1.4784325 × 109
Variance31481618
MonotonicityNot monotonic
2024-05-16T14:50:16.569360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436 65360
15.3%
7 55519
13.0%
14695 47067
11.0%
156 33234
 
7.8%
460 30400
 
7.1%
198 27548
 
6.5%
12078 20958
 
4.9%
1116 14266
 
3.3%
202 12218
 
2.9%
81 12054
 
2.8%
Other values (209) 108359
25.4%
ValueCountFrequency (%)
7 55519
13.0%
22 31
 
< 0.1%
30 203
 
< 0.1%
31 390
 
0.1%
32 93
 
< 0.1%
37 472
 
0.1%
38 4
 
< 0.1%
39 785
 
0.2%
40 2713
 
0.6%
41 1
 
< 0.1%
ValueCountFrequency (%)
15217 85
< 0.1%
15215 8
 
< 0.1%
15193 31
 
< 0.1%
15191 1
 
< 0.1%
15190 39
 
< 0.1%
15172 20
 
< 0.1%
15171 20
 
< 0.1%
15170 133
< 0.1%
15140 41
 
< 0.1%
14734 60
< 0.1%
Distinct214
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-05-16T14:50:17.168213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length82
Mean length31.41163
Min length6

Characters and Unicode

Total characters13412232
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowTermo Circunstanciado
2nd rowTermo Circunstanciado
3rd rowTermo Circunstanciado
4th rowTermo Circunstanciado
5th rowAção Penal - Procedimento Ordinário
ValueCountFrequency (%)
cível 184439
 
10.2%
procedimento 181361
 
10.0%
especial 119784
 
6.6%
de 117083
 
6.5%
do 113118
 
6.2%
juizado 112427
 
6.2%
pública 68548
 
3.8%
fazenda 68143
 
3.8%
da 58434
 
3.2%
cumprimento 57620
 
3.2%
Other values (323) 733268
40.4%
2024-05-16T14:50:18.130898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1395319
 
10.4%
e 1346148
 
10.0%
o 1106540
 
8.3%
a 945796
 
7.1%
i 887384
 
6.6%
d 776659
 
5.8%
n 699001
 
5.2%
c 567178
 
4.2%
l 524971
 
3.9%
m 520996
 
3.9%
Other values (65) 4642240
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13412232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1395319
 
10.4%
e 1346148
 
10.0%
o 1106540
 
8.3%
a 945796
 
7.1%
i 887384
 
6.6%
d 776659
 
5.8%
n 699001
 
5.2%
c 567178
 
4.2%
l 524971
 
3.9%
m 520996
 
3.9%
Other values (65) 4642240
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13412232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1395319
 
10.4%
e 1346148
 
10.0%
o 1106540
 
8.3%
a 945796
 
7.1%
i 887384
 
6.6%
d 776659
 
5.8%
n 699001
 
5.2%
c 567178
 
4.2%
l 524971
 
3.9%
m 520996
 
3.9%
Other values (65) 4642240
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13412232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1395319
 
10.4%
e 1346148
 
10.0%
o 1106540
 
8.3%
a 945796
 
7.1%
i 887384
 
6.6%
d 776659
 
5.8%
n 699001
 
5.2%
c 567178
 
4.2%
l 524971
 
3.9%
m 520996
 
3.9%
Other values (65) 4642240
34.6%

Interactions

2024-05-16T14:49:53.796048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:50.120922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:51.317868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:52.379421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:54.099663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:50.392168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:51.562248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:52.776514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:54.503354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:50.671314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:51.822622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:53.180639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:54.820382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:50.971575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:52.078822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-16T14:49:53.512171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-16T14:49:56.166462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-16T14:49:57.672387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AnoMesData de ReferenciaTribunalNome OrgaoCodigo Orgaoid_municipioMunicipioUFGrauFormatoProcedimentoProcessoCodigos assuntosCodigos classesCodigo da Ultima classe CNNome da Ultima classe CN
0202332023-03-21TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800805-11.2023.8.20.5100{10508,3659}{278}278Termo Circunstanciado
1202382023-08-31TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0803258-76.2023.8.20.5100{3386}{278}278Termo Circunstanciado
2202382023-08-31TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0803267-38.2023.8.20.5100{3542}{278}278Termo Circunstanciado
3202332023-03-08TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800636-24.2023.8.20.5100{3573,3402,3395}{278}278Termo Circunstanciado
4202322023-02-01TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800293-28.2023.8.20.5100{3386}{278,283}283Ação Penal - Procedimento Ordinário
5202312023-01-10TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800061-16.2023.8.20.5100{3402}{278}278Termo Circunstanciado
6202362023-06-28TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0802253-19.2023.8.20.5100{10949,3402}{278,283}283Ação Penal - Procedimento Ordinário
7202322023-02-02TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800307-12.2023.8.20.5100{3386}{10944,278}278Termo Circunstanciado
8202322023-02-13TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0800430-10.2023.8.20.5100{3386,3426}{278,283}283Ação Penal - Procedimento Ordinário
9202352023-05-26TJRNJUIZADO ESPECIAL CÍVEL, CRIMINAL E DA FAZENDA PÚBLICA DA COMARCA DE AÇU1108228AÇURNG1EletrônicoConhecimento criminal0801773-41.2023.8.20.5100{14684,5573}{278}278Termo Circunstanciado
AnoMesData de ReferenciaTribunalNome OrgaoCodigo Orgaoid_municipioMunicipioUFGrauFormatoProcedimentoProcessoCodigos assuntosCodigos classesCodigo da Ultima classe CNNome da Ultima classe CN
426973202422024-02-01TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(450782952){12397}{279,283}283Ação Penal - Procedimento Ordinário
4269742023122023-12-27TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(451290161){4355}{313}313Pedido de Prisão Preventiva
4269752023122023-12-19TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(451310684){11417}{283}283Ação Penal - Procedimento Ordinário
426976202412024-01-24TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(557770959){14124,10609}{310}310Pedido de Quebra de Sigilo de Dados e/ou Telefônico
426977202412024-01-24TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(577013433){10609}{11955}11955Cautelar Inominada Criminal
426978202422024-02-23TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(577020918){10914}{309}309Pedido de Busca e Apreensão Criminal
426979202422024-02-27TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(577027627){14124}{310}310Pedido de Quebra de Sigilo de Dados e/ou Telefônico
426980202422024-02-15TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNG1EletrônicoConhecimento criminalsigiloso(582010681){10609}{310}310Pedido de Quebra de Sigilo de Dados e/ou Telefônico
426981202422024-02-26TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNJEEletrônicoConhecimento criminal0800539-42.2024.8.20.5600{3633}{279,280,283}283Ação Penal - Procedimento Ordinário
426982202422024-02-01TJRN4ª VARA CRIMINAL DA COMARCA DE MOSSORÓ879223159MOSSORORNJEEletrônicoConhecimento criminal0802294-31.2024.8.20.5106{3633}{283}283Ação Penal - Procedimento Ordinário